1. Field of the Invention
The present invention generally relates to techniques for the geographic localization (geolocation) of mobile communications terminals like cellular phones, smart phones and the like based on GNSS (Global Navigation Satellite Systems). More specifically, the invention concerns a method and a system for increasing the accuracy of the estimation of the geographic position of mobile communications terminals.
2. Description of the Related Art
Determining the geographic position of (La, geographically localizing) mobile communications terminals like cellular phones, smart phones and similar handheld devices operable with mobile communications networks can be exploited in a variety of services and applications, such as location-sensitive yellow-pages services (e.g., exploiting the known location of a mobile terminal for providing to the user information about restaurants, hotels, etc. in his/her location neighborhood), services offered to communities of users (e.g., friend/family member find services), location-sensitive call billing schemes. The capability of geographically locating mobile communications terminals is also of great importance in case of emergency calls and/or for tracking specific classes of users, like for example elders and children.
Several techniques are known in the art that allow the geographic localization of mobile terminals.
Satellite-based geolocation systems like the GPS (Global Positioning System) are an effective solution for geographically localizing mobile communication terminals, since the integration of GPS receivers in smart phones is now very common.
As known in the art, the GPS comprises a “constellation” of satellites that orbit around the earth; each GPS satellite carries a GPS transmitter that transmits GPS signals encoded with information allowing GPS receivers on earth to measure the time of arrival of the received signals with respect to an arbitrary point in time: this relative time-of-arrival measurement may then be converted into a “pseudo-range” measure (a measure of the apparent propagation time from the satellite to the GPS receiver, expressed as a distance).
The position (latitude, longitude, height) and the clock offset (the offset between the receiver clock and the GPS system clock) of a GPS receiver may be accurately estimated based on a sufficient number of available pseudo-range measurements (typically four). Calculating the position (the so-called “GPS fix”) of a GPS receiver involves solving a system of equations where the unknown variables are the GPS receiver geographic coordinates to be determined and the clock offset.
The GPS performance is rather good in open-sky conditions when the number of received signals is equal to or greater than five and all the received signals are in “Line Of Sight” (LOS). Depending on the specific signal processing techniques and the algorithms for estimating the PVT (Position-Velocity-Time) implemented at the GPS receiver, the localization accuracy may in these conditions vary from approximately 2 m to few millimeters.
However, GPS signals are normally received at very low power levels, due to the relatively large distances between the satellites and the receivers, and in particular the GPS signal received by GPS receivers is highly attenuated when the receivers happen to be inside a building, under dense foliage, in urban settings in which tall buildings block much of the sky visibility (so called “urban canyons”), and so on. Since mobile communications terminals are very often used in such environments, the capability of implementing geolocation services involves the possibility of exploiting the GPS in environments where the GPS signals are highly attenuated.
The increase in sensitivity of GPS receivers has made it possible to use the GPS also not in open-sky, LOS conditions, as it is normally the case of urban and indoor environments, where there is no direct visibility of the satellites and the GPS signal is received after having been diffracted, reflected and strongly attenuated by surrounding obstacles. GPS techniques in these environments are called High-Sensitivity GPS (HSGPS).
Further improvements in the sensitivity of GPS receivers, as well as the deployment of GNSS complementary to the GPS, like Galileo, are expected to partially overcome the problem of the signals obstruction.
A critical aspect of geographic localization techniques in general, and of mobile terminals in particular, is the accuracy by which the geographic position can be established.
For example, the location of a mobile terminal making an emergency call should be determined as precisely as possible, or at least an indication of the degree of uncertainty associated with the determined location of the mobile terminal should be provided, in order to ease the task of finding where the user who placed the call is, and e.g. quickly rescue him/her. The regulatory authorities of some countries have in this respect also set forth minimum accuracy standards.
Two are the sources of the error affecting the estimation of the geographic position of a GPS receiver: the geometry of the satellites constellation and the noise affecting the pseudo-range measurements. The noise originates from thermal effects and, mainly, from multipath GPS signal propagation components that are superimposed to the main GPS signal, as well as from non-LOS propagation paths due to obstacles (which cause signal diffraction). An accurate statistical modeling of the noise would allow to significantly improve the accuracy of the position estimation. Thermal noise is white noise that can be modeled by means of a Gaussian distribution, and its impact on the code-phase measures (i.e., the measures of time delay corresponding to a phase difference between an individual scrambling code used by each satellite to modulate the signal to transmit and a corresponding scrambling code used to modulate a signal locally generated at the receiver for the signal acquisition purposes) can be estimated based on characteristic parameters of the specific signal processing algorithm implemented at the receiver. Noise originating from multipath and non-LOS signal propagation, unlike thermal noise, cannot be modeled as white noise, because it depends on the specific signal propagation environment; since this is the most relevant noise source, the capability of accurately estimating the receiver position greatly depends on the capability of modeling also this type of noise.
It has been shown that an empirical model such as to account for multipath propagation and diffraction effects may be effective and capable of providing good noise estimations. Such empirical model can be approximated by a Gaussian distribution, with average value and variance that depend on the strength of the received signal.
When a Gaussian modeling of the error affecting the pseudo-range measures is available, the position of the GPS receiver can be estimated using the Weighted Least Square (WLS) method (see for example A. Dalla Torre et al, “Analysis of the Accuracy of Indoor GNSS Measurements and Positioning Solution”, European Navigation Conference—ENC—2008, Toulouse, Apr. 23-25, 2008), which allows finding a solution to the above-mentioned system of equations which minimizes the squared error of the measures, by weighting each measure with the inverse of the square of the variance.
Kalman filtering techniques can also be adopted for enhancing the estimation of the error affecting the calculated GPS fix. Actually, the use of Kalman filtering is possible under the assumption that the errors at different times are not correlated, something that is not true in the GPS, particularly when applied to the localization of mobile GPS receivers, like those embedded in mobile communications terminals (because the mobile terminals generally move at relatively low speed, of the order of 1 m/s); nevertheless, mathematical stratagems have been proposed that allows applying Kalman filtering also in the GPS (see for example M. G. Petovello et al, “Quantifying Ambiguity Resolution in the Presence of Time-Correlated Measurement Errors Using Geometric-Based Techniques”, in Proceedings of the 61st Institute of Navigation Annual Meeting, pp. 1073-1085, Cambridge, Mass., USA, June 2005).
By using an empirical model of the noise which is valid for heterogeneous environments, the error affecting the estimated geographic position can be strongly reduced. However, it has been shown that the approximation of the error by a Gaussian distribution is sufficiently accurate and effective for errors (i.e., measure confidence intervals) not greater than 90%-95%, corresponding to approximately twice the variance (see cited paper of A. Dalla Torre et al). For errors greater than twice the variance, the distribution is completely different from a Gaussian, and the WLS method is no longer effective.
When the number of different satellite transmitter signals received by a GPS receiver is higher than the number of unknown variables in the mathematical system of equations to be solved for calculating the GPS fix (typically, when pseudo-range measurements for more than four satellites are available at the GPS receiver), the quality of the calculated GPS fix can be assessed by conducting a statistical test (so-called “integrity test”) on the post-fit residuals, which are the differences between the measured pseudo-range values and the expected pseudo-range values calculated on the basis of the obtained GPS fix. The integrity test checks whether the sum of the squares of the post-fix residuals, normalized to the hypothesized variance of the pseudo-range measurements, is less than a preset threshold, that depends on the degrees of freedom (number of available pseudo-range measurements less the number of unknown variables in the equations system to be solved). If the error can be modeled by a Gaussian distribution, the sum of the squares of the post-fix residuals has a distribution called “χ2”. For values of the sum of the squares of the post-fit residuals exceeding a given threshold the errors are no longer Gaussian and thus it is possible to determine that the WLS method is not effective.
The described technique for obtaining an estimation of the overall quality of the pseudo-range measures calls for analyzing the individual post-fit residuals to assess whether there are residuals values that do not match with those expected for a Gaussian distribution of the errors. If this test indicates that there are gross errors (“outliers”) in the pseudo-range measures, a Gaussian model cannot be adopted to describe the errors, and also in this case the WLS method is not effective.
It is known that in those cases where the error affecting measurements is not Gaussian, the Monte Carlo method can be used. In brief, the Monte Carlo method applied to PVT estimation calls for defining an n-dimensional space of possible solutions, where n is the number of variables in the system of equations to be solved; in the GPS case, the variables are the latitude, longitude, height and clock offset—the difference between the GPS time and the local time at the receiver. A sufficiently dense set of possible solutions (so-called “particles”) are defined within this space: given a set of pseudo-range measurements, each one characterized by its own distribution of the error, for each particle in such a set the respective probability is estimated depending on the measures performed by the receiver and on the error model. The final point in the space of solutions is then calculated as the weighted average of all the particles.
When the errors at different times are not correlated, it is advantageous to use the sequential Monte Carlo method, also known as “Particle Filter” method, which, like the Kalman filtering technique for Gaussian error distributions, allows enhancing the estimation of the error affecting the measurements (see for example M. Sanjeev Arulampalam et al, “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking”, IEEE Trans. On Signal Processing, Vol. 50, No. 2, February 2002). Essentially, the probability of the generic particle is calculated as the product of the probabilities estimated on the basis of the measurements made at subsequent time instants.